An ultrasound scanner is an integrated device which includes a scanning head and cooperating mechanical and electronic components, including a signal detection apparatus, for producing an ultrasound image. An ultrasound image is the result of a complicated set of physical phenomena, namely, the absorption, reflection, and coherent scattering from a tissue medium of pulsed radio frequency ultrasonic pressure waves, and the electronic detection of the backscattered or echo pulses for display as an image. The resulting pictures have a granular structure variously described by the terms texture or speckle. The resolution capability of an ultrasound scanner is its capacity to produce an image distinguishing a target object in a scanned volume, known as a slice, from the texture in the image produced from adjacent background material. An object which is resolved may be said to be detected by an ultrasound scanner. Typically, the signal detection apparatus of an ultrasound scanner includes a monitor for displaying a visual image. To be resolved, when only such a monitor is in use, an object must be detectable by an examination of such an image.
The resolution of target objects in a clinical ultrasound scan is possible because of the ultrasonic differentiation of tissue structures, primarily through variations in the scattering of ultrasonic waves by these tissues. This diffuse scattering gives rise to the texture pattern of an ultrasound image of a clinical object where variations in gray level are seen in the displayed image. Often, the mean gray level of a displayed object of interest, e.g. a tumor, will be different from that of the surrounding tissue. In other words, there is displayed image contrast between the object of interest and the background. The displayed image contrast is associated with the object contrast between the material of the object of interest and the background material. The object contrast, in turn, is a function of the difference, or ratio, of the backscatter coefficients of the two media involved, one for the object of interest material, the other for the background material.
Detectability of an object in an ultrasound image depends on three factors: (1) the size of the object, (2) the object contrast, and (3) the nature of the texture or speckle pattern of the object with respect to its background surroundings. The first two factors are determined by the material being imaged, whereas the third factor is strongly dependent on the scanner instrumentation. Thus, the smaller the object the less detectable, or resolvable, it is. At the same time, the less the object contrast, the less detectable the object is. Object contrast is based on the backscatter coefficients of the material of the object of interest, the target object or lesion, and the surrounding background material. Object contrast may be defined, in decibels, to be: EQU C=10log.sub.10 (.eta..sub.1 /.eta..sub.b) (1)
where:
C is the object contrast; PA1 .eta..sub.1 is the backscatter coefficient of the target lesion material; and PA1 .eta..sub.b is the backscatter coefficient of the surrounding background material.
The resolution performance of ultrasound scanners may be tested using ultrasound test objects known as phantoms. Most current commercially available phantoms for testing performance of ultrasound scanners contain a uniform background material which is tissue mimicking in terms of ultrasonic attenuation, propagation speed and backscatter coefficient. Such a material is described in U.S. Pat. No. 4,277,367, to Madsen, et al., entitled "Phantom Material and Method." A variety of types of phantoms are used to determine the resolution abilities of ultrasound scanners. Most of these phantoms containing objects to be detected. For example, one type of phantom contains metal or plastic fibers, arranged either individually or in pairs, oriented perpendicular to the scanning plane. An ultrasound scanner is thereby tested by its ability to detect the fibers at all, and also by a determination of the least separation between fibers such that they can still be resolved.
Another type of phantom consists of a block of gelatin containing long, thin cones or, alternatively, stepped cylinders of gelatin. Both the block of gelatin, and the target cones or cylinders within it, contain plastic beads or other particles that function as ultrasound scatterers. Each stepped cylinder contains scatterers of a different size and/or concentration relative to the scatterers contained in the background material so that the scattering level of the background material is different from that of each cylinder. The cylinders, therefore, have a controlled object contrast level. During use, the axes of the cone or cylinder shaped target objects are maintained at right angles to the scanning plane (the plane of symmetry of the scanned slice).
The cylindrical geometry of the target objects in the phantom just described results in a nearly constant object contrast being maintained over the intersection of the target cylinder with the scan slice. A shortcoming of such a geometry is that the ultrasound slice profile does not provide for a realistic determination of detectability of a target when the diameter of the cylinder is in the range of, or less than, the slice width. This is because there is almost never a clinical situation in which an object of interest has translational symmetry perpendicular to the scan plane. Thus, since no attempt is made to approximate the three-dimensional shape of the lesions commonly being searched for in clinical applications of ultrasound scanning, the significant effect of the width of the slice on detectability is not dealt with. Thus, tests involving cylindrical targets do not adequately evaluate an ultrasound scanner regarding detection of focal lesions.
It has, therefore, been recognized that a focal lesion is realistically represented with a sphere. U.S. Pat. No. 4,843,866, to Madsen, et al., entitled Ultrasound Phantom, describes an ultrasound phantom including testing spheres having backscatter coefficients different from the backscatter coefficient of the tissue mimicking material in which they are embedded. In that invention, the testing spheres are located within the phantom body in a random array in order to eliminate the possibility of user bias when resolution characteristics are being tested by human observers. Another embodiment of that invention includes a phantom body divided into multiple subsections. Each subsection may contain testing spheres differing in size or backscatter coefficient from those in the other subsections.
Evaluations of ultrasound scanner performance have been made using a phantom in which a large number of spherical simulated lesions are randomly distributed as described above. For example, these phantoms were used to assess focal lesion detectability performance of various ultrasound scanner configurations. Estimations were made by a group of human observers of the proximal (shortest) distance from the scanning head where lesions may first be resolved, and distal (farthest) distance from the scanning head where lesions become no longer resolvable, to ascertain the limits of the depth range over which lesions of a given size and contrast were detectable. This depth range of detectability may be referred to as the resolution zone. For a given image depth and object contrast there will be a minimum lesion diameter which is resolvable and for which detection exists. A graph of the contrast versus the minimum diameter of lesion detectable at a certain depth is the contrast-detail curve at that depth. Thus, if the proximal and distal limits of the depth range are known for a sufficiently broad range of lesion diameters and contrasts for a certain imager configuration, this is equivalent to knowledge of the contrast-detail curves at all depths.
There are, however, shortcomings to this method for determining the proximal and distal limits of the depth range of detectability. Firstly, the process for estimation of the proximal and distal depth ranges by an observer can be time-consuming, especially if a reasonably large set of combinations of target lesion diameters and contrasts are involved. Secondly, estimations of the proximal and distal limits of the depth range can depend on the experience of the human observer.
Therefore, although ultrasound phantoms such as those described make possible many different types of performance measurements, it is still uncommon for clinical personnel to obtain specifications, make purchase decisions, and evaluate ultrasound scanners using quantitative image performance criteria. They rely almost solely on clinical impressions when judging the performance of a scanner. A more automated method for determining focal lesion detectability would nullify these limitations, and, consequently, foster much more widespread use of these types of resolution tests.
A computer-based method for the analysis and evaluation of ultrasound images is presented in a paper by Hector Lopez, et al., "Objective Analysis of Ultrasound Images by Use of a Computational Observer", IEEE Transactions on Medical Imaging, Vol. 11, No. 4, pp. 496-506, 1992. This method employs the concept of a "matched filter" with ultrasound phantoms containing low contrast cone-shaped and essentially cylindrical targets to do objective contrast-detail studies. This matched filter technique is conceptually simple when the target object is basically cylindrical in shape, with axial symmetry of mean object contrast along the cylinder, and where the cylinder is oriented perpendicular to the ultrasound scan slice. The first step of this method is the making of an ultrasound scan image of a slice perpendicular to the cylindrical target, which is then digitized. The digital image is then sectored, and location coordinates for each target object in the image are established. The signal to noise ratio (SNR) of the target object is then calculated. A SNR cut-off point may then be established, with lesions having a SNR above the threshold considered detectable, or resolved, and those below the threshold considered not detectable.
A lesion signal to noise ratio calculated by the matched filter method, (SNR).sub.ML, is defined as: EQU (SNR).sub.ML .ident.(S.sub.ML -S.sub.MB)/(.sigma..sub.B.sup.2 +.sigma..sub.L.sup.2).sup.1/2. (2)
Note that (SNR).sub.ML does not represent a simple pixel signal to noise ratio but depends on the size of the target lesion whose detectability is being analyzed. In Equation 2, S.sub.MB reflects an average of pixel values of an image of the phantom background material. S.sub.MB is the ensemble average of a large number of independent realizations of S.sub.B, which is defined as: ##EQU1## S.sub.B is the mean of the mth power of the image pixel values over the projected (usually circular) area of the focal lesion in the image plane in an area known to contain no lesions (background). In other words, the digitized image is first examined and the area which should be occupied by a known lesion is determined. This area will contain n pixels, each having a pixel value P.sub.i. S.sub.B is then calculated over an area of n pixels in a portion of the image plane which contains background material only, and no lesions. S.sub.MB is then the average of a large number of independent calculations of S.sub.B over several such n pixel areas in areas of the image containing background material only.
This calculation may be made using either the "amplitude" or the "intensity" value of each pixel in the area containing n pixels. If m=1, then P.sub.m is called the pixel amplitude, and S.sub.B is called the pixel amplitude mean. If m=2, then P.sup.m is called the pixel intensity, and S.sub.B is called the pixel intensity mean.
In Equation 2, S.sub.ML reflects an average of the average pixel values of an image of the phantom lesion material. That is, S.sub.ML is the ensemble average of S.sub.L for independent realizations of S.sub.L where: ##EQU2## S.sub.L is the weighted mean of the mth power of the image pixel values over an area in the image corresponding to the known location of lesion. As discussed above, this area will contain n pixels, each having a pixel value P.sub.i. S.sub.L is then calculated over this same n pixel area of the image known to correspond to the position of a target lesion. S.sub.ML is then the average of independent calculations of S.sub.L over multiple n pixel areas in a set of images which are statistically independent. In other words, the speckle patterns in the ensemble of images must be statistically independent. This means that independent physical realization of the target lesions and background material must be used. If the simulated lesions are cylinders oriented perpendicularly to the scanning slice, then the images (and speckle patterns) will be statistically independent if the scan slices do not overlap one another.
W.sub.L (r.sub.i), in Equation 4, is a weighting function of the distance, r.sub.i, from the center of the circular target area over which S.sub.L is being calculated. This factor depends on the expectation value of P.sub.i relative to that of the background material. If W.sub.L (r.sub.i) is independent of the distance from the center of the lesion area, r.sub.i, corresponding to an infinitely long cylinder with uniform ultrasonic properties, including backscatter coefficient, then W.sub.L (r.sub.i)=1.
The final two components of Equation 2 are .sigma..sub.B and .sigma..sub.L. .sigma..sub.B is the standard deviation of the ensemble of S.sub.B values, and .sigma..sub.L is the standard deviation of the ensemble of S.sub.L values.